Vighnesh Birodkar
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Signal Processing
- Media Technology
- Control and Systems Engineering
- Co-authors
- Emily DentonVincent DumoulinCristina Nader VasconcelosZhichao LuSiyang LiJonathan HuangVivek RathodNatasha Jaques
- Topics
- Multimodal Machine Learning Applications (2 papers)Adversarial Robustness in Machine Learning (2 papers)Advanced Neural Network Applications (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionArtificial IntelligenceComputer Graphics and Computer-Aided Design
- Journals
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)2021 IEEE/CVF International Conference on Computer Vision (ICCV)Neural Information Processing Systems
- Partner nations
- United StatesGermanyIsrael
In The Last Decade
Vighnesh Birodkar
4 papers receiving 162 citations
Peers
Comparison fields: 5 of 44
- Computer Vision and Pattern Recognition 141
- Artificial Intelligence 60
- Signal Processing 14
- Media Technology 8
- Control and Systems Engineering 7
Countries citing papers authored by Vighnesh Birodkar
This map shows the geographic impact of Vighnesh Birodkar's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Vighnesh Birodkar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Vighnesh Birodkar more than expected).
Fields of papers citing papers by Vighnesh Birodkar
This network shows the impact of papers produced by Vighnesh Birodkar. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Vighnesh Birodkar. The network helps show where Vighnesh Birodkar may publish in the future.
Co-authorship network of co-authors of Vighnesh Birodkar
This figure shows the co-authorship network connecting the top 25 collaborators of Vighnesh Birodkar. A scholar is included among the top collaborators of Vighnesh Birodkar based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Vighnesh Birodkar. Vighnesh Birodkar is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 24 | |
| 2 | 12 | |
| 3 | 17 | |
| 4 | Unsupervised Learning of Disentangled Representations from Video | 115 |
About Vighnesh Birodkar
Vighnesh Birodkar is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Infectious Diseases, having authored 4 papers that have together received 168 indexed citations. Recurring topics across this work include Multimodal Machine Learning Applications (2 papers), Adversarial Robustness in Machine Learning (2 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (141 citations), Artificial Intelligence (60 citations) and Computer Graphics and Computer-Aided Design (6 citations). Vighnesh Birodkar has collaborated with scholars based in United States, Germany and Israel. Frequent co-authors include Emily Denton, Vincent Dumoulin, Cristina Nader Vasconcelos, Zhichao Lu, Siyang Li, Jonathan Huang, Vivek Rathod, Natasha Jaques, Austin Waters and Peter J. Anderson. Their work appears in journals such as 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021 IEEE/CVF International Conference on Computer Vision (ICCV) and Neural Information Processing Systems.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.